Category Archives: Azure Machine Learning

Different Approaches to Perform Machine Learning Experiments on Azure

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We have discussed a lot about Azure Machine Learning Studio; the one-stop portal for all ML related workloads on Azure cloud. AzureML Studio provides different approaches to work on the machine learning experiments based on needs, resources and constraints you have.  Selecting the plan to attack is completely your choice.  

We all have our own way of performing machine learning experiments. While some prefer working on Jupyter notebooks, some are more into less code environments. Being able to onboard data scientists with their familiar development environment without a big learning overhead is one of the main advantages of AzureML.

In this article, let’s have a discussion on different methods available in AzureML studio and their usage in practical scenarios. We may discuss pros and cons of each approach as well.

Please keep in mind that, these are my personal thoughts based on the experiences I had with ML experiments and this may change in different scenarios.

Automated ML

Summary of an Automated ML experiment

As the name implies, this is all automated. Automated ML is the easiest way of producing a predictive model just in few minutes. You don’t need to have any coding experience to use Automated ML. Just need to have an idea on machine learning basics and an understanding on the problem you going to solve with ML.

The process is pretty straight forward. You can start with selecting the dataset you want to use for ML model training and specify the ML task you want to perform. (Right now, it supports classification, regression, time series forecasting. Computer vision and NLP tasks are in preview). Then you can specify the algorithms you want to test it with and other optional parameters. Azure does all the hard work for you and provides a deployment ready model which can be exposed as a REST API.

Pros:

  • Zero code process.
  • Easy to use and well suited for fast prototyping.
  • Eliminate the environment setup step in ML model development
  • Limited knowledge on machine learning is needed to get a production viable result.  

Cons:

  • Limited machine learning capabilities.
  • Right now, only works with supervised learning problem scenarios.
  • Works well with relational data, computer vision and NLP are still in preview.
  • There’s no way of using custom machine learning algorithms in the process.

Azure ML Designer

Azure ML Designer

Azure ML Designer is an upgraded version of the pretty old Azure ML Studio drag and drop version. Azure ML Designer is having a similar drag and drop interface for building machine leaning experiment pipelines. You have a set of prebuilt components which you can connect together in a flowchart like manner to build machine learning experiments. You have the ability to use SQL queries or python/ R scripts if you want in the process too. After training a viable ML model, you can deploy it as a web service with just few clicks.

I personally prefer this for prototyping. Plus, I see a lot f potential on Azure ML designer in educational purposes. It’s really easy to visualize the ML process through the designer pipelines and it increases the interpretability of the operation.

Pros:

  • Zero code/ Less code environment
  • Easy to use graphical interface
  • No need to worry on development/ training environment configurations
  • Having the ability to expand the capabilities with python/ R scripts
  • Easy model deployment

Cons:

  • Less flexibility for complex ML model development.
  • Less support for deep learning workloads.
  • Code versioning should handle separately.

Azure ML notebooks

Performing data visualization on AzureML notebooks

This maybe the most favourite feature on Azure ML for data scientists. I know Jupyter notebooks are like bread and butter for data scientists. Azure ML offers a fully managed notebook experience for them without giving them the hassle of setting up the dev environment on local computers. You just have to connect the notebook with a compute instance and it allows you to do your model development and training on cloud in the same way you did on a local compute or elsewhere on notebooks.

I would recommend this as the to-go option for most of the machine learning experiments since it’s really easy to spin up a notebook instance and get the job done. Most of the ML related libraries are pre-installed on the compute instance and you even have the flexibility to install 3rd party packaged you need through conda or pip.

Pros:

  • Familiar notebook experience on cloud.
  • Option to use different python kernels.
  • No need to worry about dev environment setup on local compute.
  • Can use the powerful compute resources on Azure for model training.
  • Flexibility to install required libraries through package managers.

Cons:

  • Comes with a price for computation.
  • No direct support for spark workloads.
  • Code version control should manage separately.

Developing on local and connect to AzureML service through AML Python SDK.

This is the option I would suggest for more advanced users. Think of a scenario where you have a deep learning based computer vision experiment to run on Azure with a complex code base. If this is the case, I would definitely use AzureML python SDK and connect my prevailing code base with the AzureML service.

In this approach, your code base sits on your local computer and you are using Azure for model training, deployment and monitoring purposes. You have the total flexibility of using the power of cloud for computations as well as the flexibility of using local machine for development.

Pros:

  • Total flexibility in performing machine learning experiments with our comfortable dev tools.
  • AzureML python SDK is an open-source library.
  • Code version controlling can be handled easily.
  • Whole ML process can be managed using scripts. (Easy for automation)

Cons:

  • Setting up the local development environment may take some effort.
  • Some features are still in experimental stage.

Choosing the most convenient approach for your ML experiment is totally based on the need and resources you have. Before getting into the big picture, start small. Start with a prototype, then a workable MVP, gradually you can move forward with expanding it with complex machine learning approaches.

What’s your most preferred way of model development from these options? Please mention in the comments.

Cheers!

MLOps : Let’s start plumbing ML experiments!

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What’s all this hype on MLOps? What’s the difference between machine learning and MLOps? Is MLOps essential? Why we need MLOps? Through this article series we going to start a discussion on MLOps to get a good start with the upcoming trend. The first post is not going to go deep with technicalities, but going to cover up essential concepts behind MLOps.

What is MLOps?

As the name implies, it is obviously having some connection with DevOps. So, will see what DevOps is first.

“A compound of development (Dev) and operations (Ops), DevOps is the union of people, process, and technology to continually provide value to customers.”

Microsoft Docs

This is the formal definition of DevOps. In the simpler terms, DevOps is the approach of streamlining application development life cycle of software development process. It ensures the quality engineering and security of the product while making sure the team collaboration and coordination is managed effectively.

Imagine you are a junior level developer in a software development company who develops a mission critical system for a surveillance application. DevOps process make sure each and every code line you write is tracked, managed and integrated to the final product reliably. It doesn’t stop just by managing the code base. It involves managing all development life cycle steps including the final deployment and monitoring of the final product iteratively too.

That’s DevOps. Machine Learning Operations (MLOps) is influenced by DevOps principles and practices to increase the efficiency of machine learning workflows. Simply, it’s the way of managing ML workflows in a streamlines way to ensure quality, reliability, and interpretability of machine learning experiments.

Is MLOps essential?

We have been playing around with machine learning experiments with different tools, frameworks and techniques for a while. To be honest, most of our experiments didn’t end up in production environments :D. But, that’s the ultimate goal of predictive modeling.

Machine Learning experiment is an iterative process
Source : https://azure.microsoft.com/en-au/resources/gigaom-delivering-on-the-vision-of-mlops/

Building a machine learning model and deploying it is not a single step process. It starts with data collection and goes in an iterative life cycle till monitoring the deployed model in the production environment. MLOps approaches and concepts streamline these steps and interconnect them together.

Answer is Yes! We definitely need MLOps!

Why we need MLOps?

As I said earlier, MLOps interconnect the steps in ML life cycle and streamline the process.

I grabbed these points from Microsoft docs. As it implies, these are the goals of MLOps.

  • Faster experimentation and development of models

Good MLOps practices leads for more code and component reusability which leads for faster experiments and model development. For an example, without having separate training loops or data loading components for each experiment, we can reuse an abstract set of methods for those tasks and connect them with a machine learning pipeline for running different experiment configurations. That’s make the life easy of the developer a lot!

I do lot of experiments with computer vision. In my case, I usually have a set of abstract python methods that can be used for model training and model evaluation. When performing different experiments, I pass the required parameters to the pipeline and reuse the methods which makes the life easy with less coding hassle.    

  • Faster deployment of models into production

Machine learning model deployment is always a tricky part. Managing the endpoints and making sure the deployment environment is having all the required platform dependencies maybe hard to keep track with manual processes. A streamlines MLOps pipeline helps to manage deployments by enabling us to choose which trained model should go for production etc. by keeping track of a model registry and deployment slots.

  • Quality assurance and end-to-end lineage tracking

Maintaining good coding practices, version controlling, dataset versioning etc. ensures the quality of your experiments. Good MLOps practices helps you to find out the points where errors are occurring easily rather than breaking down the whole process. Will say your trained model is not performing well with the testing data after sometime from model deployment. That might be caused by data drift happened with time. Correctly configured MLOps pipeline can track such changes in the inference data periodically and make sure to notify such incidents.

  • Trustworthiness and ethical AI

This is one of the most important use cases of MLOps. It’s crucial to have transparency in machine learning experiments. The developer/ data scientist should be able to interpret each and every decision they took while performing the experiment. Since handling data is the key component of ML model, there should be ways to maintain correct security measures in experiments too. MLOps pipelines ensure these ethical AI principles are met by streamlining the process with a defined set of procedures.

How we gonna do this?

Now we all know MLOps is crucial. Not just having set of python scripts sitting in a notebook it’s all about interconnecting all the steps in a machine learning experiments together in an iterative process pipeline. There are many methods and approaches to go forward with. Some sits on-prem while most of the solutions are having hybrid approach with the cloud. I usually use lot of Azure services in my experiments and Azure machine learning Studio provides a one-stop workbench to handle all these MLOps workloads which comes pretty handy. Let’s start with a real-world scenario and see how we can use Azure Machine Learning Studio in MLOps process to streamline our machine learning experiments.

“Stay Hungry Stay Foolish” – Let’s Learn Machine Learning without Code!

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“Stay Hungry Stay Foolish”

Steve Jobs

This famous quote of Steve Jobs is one of the most precious quotes I always keep in my mind. Stepping into the IT industry 11 years back as a teenager, I was always eagerly waiting to push myself beyond the barriers and keep trying new things. That hunger led me to explore Artificial Intelligence, undoubtably the most used buzz word in today’s industry. I always make sure to keep myself foolish and open to learn new things.   

When I started exploring data science and related technologies 6 years ago, almost all the new things I experimented in the work life were self-taught from online resources. Even today I really enjoy going through documentations on different technologies and making myself familiar with those.

In the AI space, Microsoft Azure is a dominant player with their vast variety of tools and services. Being working with different AI related tools for years, I’m super thrilled to see the advancements on the Azure data & AI space. When the MVP cloud skills challenge was launched, I had no hesitation to go forward with Data & AI path, since I needed to sharpen up my skills and update myself with the new capabilities Azure AI is providing.

Azure AI is equipped with tools and services for anyone who’s interested in AI no matter in which expertise level the person is in. You can easily use Azure Cognitive services to add AI capabilities for your application by just calling for a REST API. If you want develop an advance machine learning/ deep learning experiment, Azure AI allows you to use your favorite open-source tools and frameworks with and adapt the power of cloud for your development.

What I actually learnt?

The challenge consists learning modules which covers most of the prominent parts in Azure AI domain including Azure machine learning, Azure cognitive services, Azure cognitive search and Bot framework. It has been a long time since I built bots. So, working with the new capabilities and functions of bot framework was a pretty good experience. In addition to that, Azure cognitive search is one of the services I’ve least used in my developments and I always wanted to give it a try. With the simple but well managed learning modules gave me a perfect star to sketch my first cognitive search application.

Here comes the most interesting part!

One of the most common questions I get when doing sessions in the community is “do we actually need to know coding to perform machine learning experiments?”

With no hesitation I say yes because Azure machine learning is offering two powerful tools for zero-code machine learning experiments. Automated machine learning supports training supervised machine learning models for classification, regression and time series forecasting. You can create and publish a machine learning experiment as a REST API with just few clicks!

Azure Machine Learning Designer provides you a simple drag and drop interface where you can create machine learning pipelines and publish those as REST endpoints. If you want advance functionalities, it allows you to add python or R code snippets inside the pipelines too.  

Machine Learning with Zero-code learning collection

I know you are super eager to learn on this zero-code machine learning development tools. Check out the Microsoft Learn collection I created specifically focusing on these two tools. Don’t forget to share your learning experience with me.

Click here for the Machine Learning with Zero-code learning collection!

Happy Learning!  

Connecting Azure SQL server with Azure Machine Learning

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Accessing data in different data sources is one of the main tasks in machine learning model development life cycle. Let’s discuss one of the most common data accessing scenarios.

Scenario :

We have to set of relational data points stored in a Azure SQL server to develop a machine learning model using Azure Machine Learning. Let’s see how to leverage data stored in an Azure SQL database in an Azure Machine Learning experiment.

The process contains three main steps.

  1. Set access permissions of Azure SQL database
  2. Connect Azure SQL database to an Azure ML datastore
  3. Register the data in datastore as an Azure ML dataset.

1. Set access permissions of Azure SQL database

Allow Azure services and resources to access this server

By default Azure SQL databases are protected with a firewall which limits outside access for data. Since we going to provide access for the traffic from Ips belongs to Azure resources and services, make sure you allow Azure services to access your SQL server.

2. Connect Azure SQL database to an Azure ML datastore

Azure ML datastores can be defined as the abstraction of data sources for the ML workspace or as the interconnection between the data resource and AzureML workspace.

Go to your Azure Machine Learning Studio (ml.azure.com) and click ‘New datastore’. Provide a datastore name and select ‘Azure SQL database’ as the datastore type. Make sure to authenticate the access with Azure SQL server’s user ID and the password.

Register a new datastore

3. Register the data in datastore as an Azure ML dataset.

AzureML supports two types of datasets (Take a look here to get an overview on the difference between those). Since we are dealing with a set of relational data, Tabular dataset is the option we have to use for creating the dataset.

Create dataset from datastore

Select ‘Create dataset’ from ‘Datasets’ tab on AML Studio and prmopt to ‘From datastore’ option.

Select the datastore we created in the previous step which establish the connection between AML workspace and the data source.

Provide the required SQL query to select the required data from SQL server. Make sure to validate the data before configuring the schema.

Preview dataset

All done! Now you have the access to the data in your Azure SQL database from AzureML workspace. You can easily refer this in your experiments.

Validate dataset

In the cases where your database is getting updated time to time, what you have to do is refreshing the dataset to fetch the newest data points specified by the SQL query.

Different Computation Options on Azure Machine Learning

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In a later article we discussed on different data storage methods we can use with Azure Machine learning. In this article am gonna briefly discuss different computation options we have with Azure ML.

Since computation power is one of the key advantage we get from cloud based machine learning, choosing correct computation resource for our machine learning experiments is important.

AzureML offers 4 main compute types.

01. Compute instances –

If you don’t wanna spend the time in setting up your local computer for doing the ML experiments or you wanna leverage GPUs or powerful CPUs for doing your experiments, Azure Compute instances offer fully managed virtual machines loaded with most of the essential frameworks /libraries for performing machine learning and data science experiments. When you using AzureML notebooks (jupyter notebook instance attached for AzureML), compute instance is the place where the jupyter notebook is running.

Different methods can be used to access compute instances

You can access the compute instances using different methods. Accessing through Jupyter notebooks and JupyterLab is the all time favourite of most of the data scientists. If you are a R folk, you can use Rstudio with the compute instances. Accessing the compute instance through SSH is really useful (you may have to enable SSH access when creating the compute instance) in occasions where you have to install custom packages and such for the compute instance. (The machine is ubuntu based and you can use all bash scripts there!)

Basically compute instance can be defined as a virtual machine fully loaded with data science and machine learning essentials which you can use right out of the box.

02. Compute clusters –

Compute clusters are different from compute instances with their ability of having one or more compute nodes. These compute nodes can be created with our desired hardware configurations.

Why having more than one node? That comes with the ability of using parallel processing for computations. If you are going do to hyperparameter tuning/ GPU based complex computations/ several machine learning runs at once you may have to create a compute cluster.

If you are running Automated Machine Learning expriment with AzureML, you must have a compute cluster to perform computations.

When selecting the node configurations, you can either go with CPU based nodes or GPU based nodes. GPU based nodes (NC type etc.) is bit pricy. If you are not using GPU based computing, don’t waste your dollars by just creating a compute cluster with some fancy configs.

One other key setting is ‘Virtual machine priority’. If you are ok with pushing your experiment to the cloud and get the result without a hurry, you can go with low priority nodes, which will save you a lot of dollars rather than using dedicated VMs. No harm is gonna happen for the experimentation accuracy and such.

03. Inference clusters –

There are two options to deploy Azure machine learning web services as REST endpoints. 1) Use ACI (Azure Container Instances) 2) Use AKS (Azure Kubernetes Service)

Deploying the REST web service on ACI is good for testing and development uses and AKS would be the to-go for production level large deployments. You can configure the AKS cluster according to your need through AzureML as well as from the Azure portal. These AKS clusters are pretty much similar for AKS clusters you worked in any other Azure based deployments.

04. Attached compute –

Azure machine learning is not limited for doing computations on compute clusters. You can attach Azure Databricks, Data Lake Analytics, HDInsight or a prevailing VM as a compute for your workspace. Keep in your mind that Azure machine learning only supports virtual machines running Ubuntu. These compute targets will not be managed by Azure Machine Learning itself. So you may have to perform some additional steps to make sure they are compatible with your experiments.

Choosing the correct compute resource is a key component in the success of developing machine learning experiments. On the other hand, bad computation choices may leave you with huge Azure bills! 😀

There’s no hard bound rules on selecting different compute options for your machine learning life cycle. Just make sure you use the right tool at the right time.

AzureML Python SDK – Installation & Configuration

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In the last blog post, we discussed on developing a machine learning classifier with Azure machine learning service. As mentioned there, we going to utilize the familiar development tools and frameworks for model development.

Key areas of the SDK include:

  • Explore, prepare and manage the lifecycle of your datasets used in machine learning experiments.
  • Manage cloud resources for monitoring, logging, and organizing your machine learning experiments.
  • Train models either locally or by using cloud resources, including GPU-accelerated model training.
  • Use automated machine learning, which accepts configuration parameters and training data. It automatically iterates through algorithms and hyperparameter settings to find the best model for running predictions.
  • Deploy web services to convert your trained models into RESTful services that can be consumed in any application.

~ Ref : https://docs.microsoft.com/en-us/python/api/overview/azure/ml/?view=azure-ml-py

AzureML python SDK acts as the connector between all the resources on the cloud and the dev environment.

Installing Python SDK –

AzureML SDK can be easily installed for your local computer through pip. Refer this guide for the installation process. I’d suggest to go with default installation since it’s enough for the most of the operations we used in the experiment.  It’s a good idea to upgrade the SDK before running an experiment since the package is rapidly updating.

Download config file –

For connecting the AzureML workspace, we may need the Azure subscription ID, resource group which the workspace has been created and the workspace name. The easiest way to grab these details is downloading the config.json file from the Azure portal. Place this file inside the experiment directory.

Downloading config.json from Azure portal

Connect AzureML Workspace –

Connecting the AzureML workspace and and listing the resources can be done by using easy python syntaxes of AzureML SDK (A sample code is provided below). Refer Python SDK documentation to do modifications for the resources of the AML service.

#!pip install --upgrade azureml-sdk[notebooks]
import azureml.core
from azureml.core import Workspace
from azureml.core import ComputeTarget, Datastore, Dataset

print("Ready to use Azure ML", azureml.core.VERSION)
ws = Workspace.from_config()
print(ws.name, "loaded")

#View resources in the workspace 
print("Compute Targets:")
for compute_name in ws.compute_targets:
    compute = ws.compute_targets[compute_name]
    print("\t", compute.name, ":", compute.type)
    
print("Datastores:")
for datastore in ws.datastores:
    print("\t", datastore)
    
print("Datasets:")
for dataset in ws.datasets:
    print("\t", dataset)

print("Web Services:")
for webservice_name in ws.webservices:
    webservice = ws.webservices[webservice_name]
    print("\t", webservice.name)

In next blog article, will discuss the data loading and experiment creation.

Build a Machine Learning Classifier with Azure Machine Learning Service

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Azure Machine Learning Service is becoming the one-stop place for managing all ML related workloads in Azure cloud. There are two main advantages of using Azure Machine Learning Service for your ML and data science experiments.

#1 – You can mange the whole machine learning workflow in a single environment. From data wrangling to machine learning service deployment, everything is managed on the cloud with its reliable, scalable and efficient services.

#2 – You can use your familiar open source toolset, languages and frameworks in model development. Being a ML engineer or a data scientist, you may be using python or R as your main development languages. Azure machine learning allows you to use any of those languages and frameworks to develop the experiments.

Pima Indians Diabetes Classification is one of the most famous machine learning experiments. It’s a binary classification problem which use a .CSV based tabular dataset as the input. I’ll walk you through the process I went through to perform my experiment.

Scenario:

  • Diabetes dataset is available as a .CSV file in your local file system.
  • I have to build a binary classifier trained with the dataset and deploy it as a web service with a REST endpoint.

Solution:

As shown in the diagram I used the services and tools in AMLS with my typical development environments to build up the solution.

  • Step 1: Since the experiment is going to build on Azure cloud, I have transferred my dataset into an Azure blob storage. I used Azure storage Explorer to upload the dataset into the cloud. (For better performance, make sure the dataset is in a storage blob in the same region where the AMLS experiment is)
  • Step 2: In order to access the data stored in the blob space, it’s registered inside AMLS as a datastore.
  • Step 3: AMLS supports two types of datasets. Since the .CSV file contains tabular data, it’s registered as a tabular dataset. (You can perform the basic statistical operations and visualizations after registering as a tabular dataset.)
  • Step 4: Now it’s the time for the real job. Since am more familiar with Python and sci-kit learn, I used those languages and libraries to develop my model. The whole coding part has been done on a Linux machine using my favorite VSCode IDE. 😉 You may wonder how I’m going to connect the code base on my local machine with the cloud… Here’s the place where AzureML python SDK comes to the rescue.
  • Step 5: I don’t have enough computation power to do the model training on my machine. So that, I use an Azure compute cluster to perform the computation. (In my experiment I did hyperparameter tuning to select the best parameters. Using the compute cluster allowed me to perform parallel training)
  • Step 6: After model training and getting the desired inference accuracy, I had the need of exposing the binary classification model as a web service. For that, I used Azure Container Instance (ACI) since this is going to be a small testing experiment. (I may have to go for an Azure Kubernetes Services (AKS) if I wanna go for global massive deployment)

Yp! It’s just a simple 6 step process. Complex? Don’t worry, I’m going to walk you through the whole process assisted with the code snippets in the upcoming blog posts. Stay tuned. Let’s start a real experiment with Azure Machine Learning Service.

Zero-code Predictive Model Development with AutomatedML on Azure Machine Learning

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Designing and implementing predictive experiments requires an understanding about the problem domain as well as the knowledge on machine learning algorithms and methodologies. Extensive knowledge on programming is a necessity when it comes to real-world machine learning model training and implementations.

Automated machine learning is capable of training and tuning a machine learning model for a given dataset and specified target metrics by selecting the appropriate algorithms and parameters by its own.  Azure Machine Learning offers a user-friendly wizard-like Automated ML feature for training and implementing predictive models without giving you the burden of algorithm and hyperparameter selection.

Azure Automated ML comes handy, where you are able to implement a complete machine learning pipeline without a single line of coding. It saves a time and compute resources since the model tuning is done by following data science best practices.

Azure machine learning currently supports three types of machine learning user cases in their AutomatedML pipeline.

1. Classification – To predict one of several categories in the target column

2. Time series forecasting – To predict values based on time

3. Regression – To predict continuous numeric values

Let’s go through the step by step process of developing a machine learning experiment pipeline with Azure Automated ML.

01. CREATE AN AZURE MACHINE LEARNING WORKSPACE

Azure Machine Learning Workspace is the resource you create on Azure to perform all machine learning related activities on the cloud. The steps are straight forward same as creating any other Azure resource. Make sure you create the Workspace edition is ‘Enterprise’ since AutoML is not available in the basic edition.

02. CREATE AUTOMATEDML EXPERIMENT

Create AutomatedML experiment

ml.azure.com web interface is the one stop portal for accessing all the tools and services related to machine learning on Azure. You have to create a new Automated ML run by selecting Automated ML on the Author section of the left pane.

03. SELECT DATASET

Select dataset from the source

As of now, AutomatedML supports tabular data formats only. You can upload your dataset from the local storage, import from a registered datastore, fetch from a web file or else retrieve from Azure open datasets.

04. CONFIGURE RUN

Configuring the Automated ML run

In this section you have to specify the target column of the experiment. If it’s a classification task this should be the column that indicates the class values and if it’s regression that’s the column where the numerical value to be predicted. Select a training cluster where the experiments going to run. Make sure you select a cluster that is enough for the complexity of the dataset you provided.

05. SELECT TASK TYPE AND SETTINGS

Select the task type

Select the task type that is appropriate for the dataset you selected. If you have textual data in your dataset you can enable deep learning (which is in preview) to extract the features.

In the settings of the run, you can specify the evaluation metrics, any algorithms that you are don’t want to use, validation type, exit criterion etc. for the experiment. If you wish to select only a specific set of features in the provided dataset you can configure that through the settings.

Configuring the evaluation metrics, algorithms to block, validation type, exit criterion

Running the experiment may take some time depending on the complexity of the dataset, algorithms you use and the exit criterion you used.

When the run is completed AzureML provides a summary of the run by indicating the best performing algorithm. You can directly deploy or download the best performing model as a .pkl file from the portal.

Details of the run after the completion

Deployment comes as a REST API which runs on an Azure Kubernetes Service (AKS) or Azure Container Instance (ACI).

AutomatedML comes handy when you need to do fast prototyping for a specific set of data and supports the agile process of intelligent application development. Will look on the other tools and features we have on Azure AI stack in the coming articles.

Reference : https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

Microsoft Build 2020 : AI Announcements

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The global pandemic situation has changed the world’s view on technological interventions and innovations. Carrying their corporate mission of “Empowering every person and every organization on the planet to achieve more”, the big tech giant Microsoft hold their annual developer conference “Build 2020” as a 48-hour virtual event.

Being virtual didn’t get down the excitement the conference creates among the developer community as well as within the enterprises. I blogged about few exciting announcements Microsoft did on Build 2020 related to AI on Kodez blog. Let’s discuss some on the interesting use cases of these announcements in future posts.

Read my post on Kodez here.

Data Labeling in Azure ML Studio

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One of the most time-consuming tasks in the machine learning model development pipeline is data labeling. When it comes to a computer vision related task which may use deep learning methodologies, you may need thousands of labelled data to train your models.

Create a Data Labeling project on Azure ML Studio

Azure Machine Learning offers a new feature for data labeling tasks specifically designed for computer vision related applications. Right now, AzureML supports 3 types of data labeling tasks.

  1. Image Classification multi-label – If the images in the image set is having more than one label for the image, this is the task type to go with.
  2. Image Classification multi-class – This is the simple image classification type tasks, where each image is having a single label and the dataset is having multiple labels
  3. Object Identification (Bounding Box) – If you need to have annotations for set of images to train a model for object detection, you may have to have bounding box annotations. This is the task type you should choose for such tasks.
Selecting the data labeling task type

Data labeling feature is available in both basic and enterprise versions of AzureML. Despite Basic version is not having the capability of ML assisted data labeling where a ML model is automatically built to assist the labeling process. ML assisted data labeling need a GPU based compute resource for performing model training and inferencing. (Obviously this comes with a cost then 😉 )   

There are two ways that you can add the images to build the dataset.

  1. Upload the image files into an Azure blob storage and register it as a DataStore in Azure ML (I’d recommend this since it may bypass the storage restrictions of the default storage)
  2. Upload directly for the default storage    

One of the attractive features I’ve seen in the data labeling process is the ability to use keyboard shortcuts which makes the process much more user friendly.

Data Labeling interface of a bounding box annotation task

The annotation files can be exported as a JSON which follows the COCO dataset format (This file is saved in the default blob storage of the experiment) or else can be registered as an Azure ML dataset. The progress of the data labeling project can be monitored through the dashboard on AzureML Studio.

Seems like Microsoft is having more plans on developing this product further and hope there would be interesting additions in the coming future.

Official documentation : https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-labeling-projects